Proceedings of the Annual Meeting of the Cognitive Science Society

Complex cognitive tasks, such as science inquiry, often involve a sequence of goals, each of which is pursuedthrough a sequence of actions. Effective assessment of inquiry performance requires identification of these student goals.Markov decision processes (MDPs) have been used to infer goals and beliefs over a single directed sequence of actions (Bakeret al., 2009), but multi-goal complex systems are computationally prohibitive to model. This research investigates the useof targeted MDPs as goal detectors, embedded within a larger hidden Markov model (HMM) that accounts for the transitionbetween goals. This multi-layer approach allows the MDP state spaces to remain small while modeling complex cognition.Because canonical HMM estimation is complicated by the dynamic nature of MDPs, in which action probabilities depend oncontext, we explore several different estimation methods. The approach is applied to log-file data of test-taker interactions witha simulation-based science inquiry assessment.